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Aptamer-based optical manipulation of protein subcellular localization in cells.

ABSTRACT: Protein-dominant cellular processes cannot be fully decoded without precise manipulation of their activity and localization in living cells. Advances in optogenetics have allowed spatiotemporal control over cellular proteins with molecular specificity; however, these methods require recombinant expression of fusion proteins, possibly leading to conflicting results. Instead of modifying proteins of interest, in this work, we focus on design of a tunable recognition unit and develop an aptamer-based near-infrared (NIR) light-responsive nanoplatform for manipulating the subcellular localization of specific proteins in their native states. Our results demonstrate that this nanoplatform allows photocontrol over the cytoplasmic-nuclear shuttling behavior of the target RelA protein (a member of the NF-?? family), enabling regulation of RelA-related signaling pathways. With a modular design, this aptamer-based nanoplatform can be readily extended for the manipulation of different proteins (e.g., lysozyme and p53), holding great potential to develop a variety of label-free protein photoregulation strategies for studying complex biological events.

SUBMITTER: Xie S

PROVIDER: S-EPMC7067792 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Aptamer-based optical manipulation of protein subcellular localization in cells.

Xie Sitao S Du Yulin Y Zhang Yu Y Wang Zhimin Z Zhang Dailiang D He Lei L Qiu Liping L Jiang Jianhui J Tan Weihong W

Nature communications 20200312 1

Protein-dominant cellular processes cannot be fully decoded without precise manipulation of their activity and localization in living cells. Advances in optogenetics have allowed spatiotemporal control over cellular proteins with molecular specificity; however, these methods require recombinant expression of fusion proteins, possibly leading to conflicting results. Instead of modifying proteins of interest, in this work, we focus on design of a tunable recognition unit and develop an aptamer-bas ...[more]

PMID: 32165631

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Project description:BackgroundThe study of protein subcellular localization (PSL) is important for elucidating protein functions involved in various cellular processes. However, determining the localization sites of a protein through wet-lab experiments can be time-consuming and labor-intensive. Thus, computational approaches become highly desirable. Most of the PSL prediction systems are established for single-localized proteins. However, a significant number of eukaryotic proteins are known to be localized into multiple subcellular organelles. Many studies have shown that proteins may simultaneously locate or move between different cellular compartments and be involved in different biological processes with different roles.ResultsIn this study, we propose a knowledge based method, called KnowPredsite, to predict the localization site(s) of both single-localized and multi-localized proteins. Based on the local similarity, we can identify the "related sequences" for prediction. We construct a knowledge base to record the possible sequence variations for protein sequences. When predicting the localization annotation of a query protein, we search against the knowledge base and used a scoring mechanism to determine the predicted sites. We downloaded the dataset from ngLOC, which consisted of ten distinct subcellular organelles from 1923 species, and performed ten-fold cross validation experiments to evaluate KnowPred site's performance. The experiment results show that KnowPred site achieves higher prediction accuracy than ngLOC and Blast-hit method. For single-localized proteins, the overall accuracy of KnowPred site is 91.7%. For multi-localized proteins, the overall accuracy of KnowPred site is 72.1%, which is significantly higher than that of ngLOC by 12.4%. Notably, half of the proteins in the dataset that cannot find any Blast hit sequence above a specified threshold can still be correctly predicted by KnowPred site.ConclusionKnowPred site demonstrates the power of identifying related sequences in the knowledge base. The experiment results show that even though the sequence similarity is low, the local similarity is effective for prediction. Experiment results show that KnowPred site is a highly accurate prediction method for both single- and multi-localized proteins. It is worth-mentioning the prediction process of KnowPred site is transparent and biologically interpretable and it shows a set of template sequences to generate the prediction result. The KnowPred site prediction server is available at http://bio-cluster.iis.sinica.edu.tw/kbloc/.

Project description:BackgroundProtein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming; thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins.ResultsWe propose a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machines (SVM) model and a structural homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. In the structural homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% and 93.2% using ten-fold cross-validation on the benchmark data sets. In the assessment of the evaluation data sets, our method also attains accurate prediction accuracy of 84.0%, especially when testing on sequences with a low level of homology to the training data. A three-way data split procedure is also incorporated to prevent overestimation of the predictive performance. In addition, we show that the prediction accuracy should be approximately 85% for non-redundant data sets of sequence identity less than 30%.ConclusionOur results demonstrate that biological features derived from Gram-negative bacteria translocation pathways yield a significant improvement. The biological features are interpretable and can be applied in advanced analyses and experimental designs. Moreover, the overall accuracy of combining the structural homology approach is further improved, which suggests that structural conservation could be a useful indicator for inferring localization in addition to sequence homology. The proposed method can be used in large-scale analyses of proteomes.

Dataset Information

Aptamer-based optical manipulation of protein subcellular localization in cells.

Publications

Aptamer-based optical manipulation of protein subcellular localization in cells.

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